Done.
[ NOTE, FIGURE ]: Red horizontal line indicates a signed R^2 of 0.9
Setting soft-thresholding power to: 15.
Power-transforming the gene-gene similarity matrix...Done.
---------------------------------------------------
4. Convert into topological overlap matrix (dissTOM)
---------------------------------------------------
Creating dissTOM...Done.
Performing hierarchical clustering on dissTOM...Done.
---------------------------------------------------
5. Identify modules (clusters)
---------------------------------------------------
Merging modules that have a correlation ≥ 0.9 ...Done.
[ NOTE, FIGURE ] Plotting identified clusters before and after merging.
Done.
[ NOTE, FIGURE ]: Red horizontal line indicates a signed R^2 of 0.9
Setting soft-thresholding power to: 15.
Power-transforming the gene-gene similarity matrix...Done.
---------------------------------------------------
4. Convert into topological overlap matrix (dissTOM)
---------------------------------------------------
Creating dissTOM...Done.
Performing hierarchical clustering on dissTOM...Done.
---------------------------------------------------
5. Identify modules (clusters)
---------------------------------------------------
Merging modules that have a correlation ≥ 0.9 ...Done.
[ NOTE, FIGURE ] Plotting identified clusters before and after merging.
Module (cluster) size:
mergedColors
bisque4 black blue brown brown4
87 737 403 471 88
cyan darkgreen darkgrey darkmagenta darkolivegreen
174 994 132 110 110
darkorange darkred darkseagreen4 darkslateblue darkturquoise
128 136 50 236 134
floralwhite green greenyellow honeydew1 ivory
192 302 177 53 92
lavenderblush3 lightcyan lightcyan1 lightpink4 lightsteelblue1
57 157 93 57 93
lightyellow maroon mediumpurple3 midnightblue navajowhite2
144 67 93 161 69
orange orangered4 paleturquoise palevioletred3 pink
129 93 115 70 445
plum1 plum2 purple red royalblue
94 84 334 380 140
saddlebrown salmon salmon4 skyblue steelblue
116 175 71 116 116
thistle2 violet white
81 112 127
Merging modules that have a correlation ≥ 0.85 ...Done.
[ NOTE, FIGURE ] Plotting identified clusters before and after merging.
Module (cluster) size:
mergedColors
black brown4 cyan darkmagenta darkolivegreen
2839 88 846 110 203
darkorange darkred darkseagreen4 darkslateblue darkturquoise
128 136 50 236 134
green greenyellow honeydew1 lavenderblush3 lightcyan
837 177 53 57 157
lightcyan1 lightpink4 lightsteelblue1 lightyellow maroon
93 236 93 144 67
midnightblue navajowhite2 orangered4 palevioletred3 plum2
161 69 93 186 84
red royalblue saddlebrown salmon salmon4
380 140 116 175 71
steelblue thistle2 violet white
116 81 112 127
Merging modules that have a correlation ≥ 0.8 ...Done.
[ NOTE, FIGURE ] Plotting identified clusters before and after merging.
Module (cluster) size:
mergedColors
brown4 darkgreen darkmagenta darkolivegreen darkorange
88 2839 110 203 1435
darkred darkseagreen4 darkturquoise green greenyellow
136 50 489 837 413
honeydew1 lavenderblush3 lightcyan lightcyan1 lightpink4
53 57 157 93 422
lightsteelblue1 maroon midnightblue navajowhite2 orangered4
93 67 161 256 233
saddlebrown salmon violet
116 175 112
Merging modules that have a correlation ≥ 0.75 ...Done.
[ NOTE, FIGURE ] Plotting identified clusters before and after merging.
Done.
[ NOTE, FIGURE ]: Red horizontal line indicates a signed R^2 of 0.9
Setting soft-thresholding power to: 15.
Power-transforming the gene-gene similarity matrix...Done.
---------------------------------------------------
4. Convert into topological overlap matrix (dissTOM)
---------------------------------------------------
Creating dissTOM...Done.
Performing hierarchical clustering on dissTOM...Done.
---------------------------------------------------
5. Identify modules (clusters)
---------------------------------------------------
Merging modules that have a correlation ≥ 0.9 ...Done.
[ NOTE, FIGURE ] Plotting identified clusters before and after merging.
Done.
[ NOTE, FIGURE ]: Red horizontal line indicates a signed R^2 of 0.9
Setting soft-thresholding power to: 15.
Power-transforming the gene-gene similarity matrix...Done.
---------------------------------------------------
4. Convert into topological overlap matrix (dissTOM)
---------------------------------------------------
Creating dissTOM...Done.
Performing hierarchical clustering on dissTOM...Done.
---------------------------------------------------
5. Identify modules (clusters)
---------------------------------------------------
Merging modules that have a correlation ≥ 0.9 ...Done.
[ NOTE, FIGURE ] Plotting identified clusters before and after merging.
trash <- purrr::map( sample.names,function(x) {writeLines(" ##################################################### How many genes are in each of my geneset of interest? #####################################################")## MAKE YOUR LIST OF GENES OF INTEREST ### LIST ONE - WGCNA modules list1 <- l_module_genes[[x]]sapply(list1, length) |>print()## LIST TWO - rhythmic genes list2 <- l_rhy_genes[[x]]sapply(list2, length) |>print()## CHECK FOR OVERLAP# define size of genome size =length(unique(c(unlist(list1), unlist(list2))))# make a GOM object gom.1v2 <- GeneOverlap::newGOM( list2, list1,genome.size = size ) GeneOverlap::drawHeatmap( gom.1v2,adj.p =TRUE,cutoff=0.05,what="odds.ratio",# what="Jaccard",log.scale = T,note.col ="black",grid.col ="Oranges" ) })
#####################################################
How many genes are in each of my geneset of interest?
#####################################################
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15
456 151 98 164 144 796 56 143 2809 608 83 53 255 3025 52
ARS empJTK GeneCycle JTK meta2d RAIN
1383 482 538 254 1059 870
#####################################################
How many genes are in each of my geneset of interest?
#####################################################
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15
88 3441 93 175 57 489 422 390 396 2102 620 67 112 50 93
ARS empJTK GeneCycle JTK meta2d RAIN
3311 872 844 328 2434 781
#####################################################
How many genes are in each of my geneset of interest?
#####################################################
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16
1817 336 3136 359 202 168 59 283 724 74 240 60 84 52 1031 264
ARS empJTK GeneCycle JTK meta2d RAIN
2535 725 605 299 1655 809
#####################################################
How many genes are in each of my geneset of interest?
#####################################################
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14
208 1995 1834 515 259 124 1667 592 182 89 54 152 1525 430
ARS empJTK GeneCycle JTK meta2d RAIN
3319 1281 1039 707 2934 1712